Hardware for recognition of human activities: a review of smart home and AAL related technologies

Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL—smartphones, wearables, video, and electronic components—and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard

Group-In: Group Inference from Wireless Traces of Mobile Devices

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 202

Poverty and Deprivation in Dumfries and Galloway: A Spatial Approach

This study was commissioned and funded by Dumfries and Galloway Council to provide evidence and analysis of the nature and patterns of poverty and deprivation across the region. It complements the Dumfries and Galloway Regional Economic Strategy Baseline Study and Regional Economic Profile published in 2014 and the four Area Profiles published earlier in the year (2015), and has been used to inform the development of the region’s first Anti-Poverty Strategy. It is consistent with best practice in policy development by providing quantitative data, drawn from national and local sources and qualitative information, and qualitative data drawn from Discussion Groups with people experiencing poverty across the region. The findings will be used to inform Elected Members, officers and partners throughout the implementation of the Anti-Strategy over the coming five years and also in its final evaluation

Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: systematic review, statistical modelling and survey.

© Queen’s Printer and Controller of HMSO 2014Background: The National Institute for Health and Care Excellence recommends the use of generic preference-based measures (GPBMs) of health for its Health Technology Assessments (HTAs). However, these data may not be available or appropriate for all health conditions. Objectives: To determine whether GPBMs are appropriate for some key conditions and to explore alternative methods of utility estimation when data from GPBMs are unavailable or inappropriate. Design: The project was conducted in three stages: (1) A systematic review of the psychometric properties of three commonly used GPBMs [EQ-5D, SF-6D and Health Utilities Index Mark 3 (HUI3)] in four broadly defined conditions: visual impairment, hearing impairment, cancer and skin conditions. (2) Potential modelling approaches to ‘map’ EQ-5D values from condition-specific and clinical measures of health [European Organisation for Research and Treatment of Cancer Quality-of-life Questionnaire Core 30 (EORTC QLQ-C30) and Functional Assessment of Cancer Therapy – General Scale (FACT-G)] are compared for predictive ability and goodness of fit using two separate data sets. (3) Three potential extensions to the EQ-5D are developed as ‘bolt-on’ items relating to hearing, tiredness and vision. They are valued using the time trade-off method. A second valuation study is conducted to fully value the EQ-5D with and without the vision bolt-on item in an additional sample of 300 people. Main outcome measures: Comparisons of EQ-5D, SF-6D and HUI3 in four conditions with various generic and condition-specific measures. Mapping functions were estimated between EORTC QLQ-C30 and FACT-G with EQ-5D. Three bolt-ons to the EQ-5D were developed: EQ + hearing/vision/tiredness. A full valuation study was conducted for the EQ + vision. Results: (1) EQ-5D was valid and responsive for skin conditions and most cancers; in vision, its performance varied according to aetiology; and performance was poor for hearing impairments. The HUI3 performed well for hearing and vision disorders. It also performed well in cancers although evidence was limited and there was no evidence in skin conditions. There were limited data for SF-6D in all four conditions and limited evidence on reliability of all instruments. (2) Mapping algorithms were estimated to predict EQ-5D values from alternative cancer-specific measures of health. Response mapping using all the domain scores was the best performing model for the EORTC QLQ-C30. In an exploratory analysis, a limited dependent variable mixture model performed better than an equivalent linear model. In the full analysis for the FACT-G, linear regression using ordinary least squares gave the best predictions followed by the tobit model. (3) The exploratory valuation study found that bolt-on items for vision, hearing and tiredness had a significant impact on values of the health states, but the direction and magnitude of differences depended on the severity of the health state. The vision bolt-on item had a statistically significant impact on EQ-5D health state values and a full valuation model was estimated. Conclusions: EQ-5D performs well in studies of cancer and skin conditions. Mapping techniques provide a solution to predict EQ-5D values where EQ-5D has not been administered. For conditions where EQ-5D was found to be inappropriate, including some vision disorders and for hearing, bolt-ons provide a promising solution. More primary research into the psychometric properties of the generic preference-based measures is required, particularly in cancer and for the assessment of reliability. Further research is needed for the development and valuation of bolt-ons to EQ-5D.UK Medical Research Council (MRC) as part of the MRC-NIHR methodology research programme (reference G0901486

Clinical and economic evaluation of laparoscopic surgery compared with medical management for gastro-oesophageal reflux disease : 5-year follow-up of multicentre randomised trial (the REFLUX trial)

Peer reviewedPublisher PD

The Limited Effect of Graphic Elements in Video and Augmented Reality on Children’s Listening Comprehension

There is currently significant interest in the use of instructional strategies in learning environments thanks to the emergence of new multimedia systems that combine text, audio, graphics and video, such as augmented reality (AR). In this light, this study compares the effectiveness of AR and video for listening comprehension tasks. The sample consisted of thirty-two elementary school students with different reading comprehension. Firstly, the experience, instructions and objectives were introduced to all the students. Next, they were divided into two groups to perform activities—one group performed an activity involving watching an Educational Video Story of the Laika dog and her Space Journey available by mobile devices app Blue Planet Tales, while the other performed an activity involving the use of AR, whose contents of the same history were visualized by means of the app Augment Sales. Once the activities were completed participants answered a comprehension test. Results (p = 0.180) indicate there are no meaningful differences between the lesson format and test performance. But there are differences between the participants of the AR group according to their reading comprehension level. With respect to the time taken to perform the comprehension test, there is no significant difference between the two groups but there is a difference between participants with a high and low level of comprehension. To conclude SUS (System Usability Scale) questionnaire was used to establish the measure usability for the AR app on a smartphone. An average score of 77.5 out of 100 was obtained in this questionnaire, which indicates that the app has fairly good user-centered design

Aspirin for prophylactic use in the primary prevention of cardiovascular disease and cancer : a systematic review and overview of reviews

Background: Prophylactic aspirin has been considered to be beneficial in reducing the risks of heart disease and cancer. However, potential benefits must be balanced against the possible harm from side effects, such as bleeding and gastrointestinal (GI) symptoms. It is particularly important to know the risk of side effects when aspirin is used as primary prevention - that is when used by people as yet free of, but at risk of developing, cardiovascular disease (CVD) or cancer. In this report we aim to identify and re-analyse randomised controlled trials (RCTs), systematic reviews and meta-analyses to summarise the current scientific evidence with a focus on possible harms of prophylactic aspirin in primary prevention of CVD and cancer. Objectives: To identify RCTs, systematic reviews and meta-analyses of RCTs of the prophylactic use of aspirin in primary prevention of CVD or cancer. To undertake a quality assessment of identified systematic reviews and meta-analyses using meta-analysis to investigate study-level effects on estimates of benefits and risks of adverse events; cumulative meta-analysis; exploratory multivariable meta-regression; and to quantify relative and absolute risks and benefits. Methods: We identified RCTs, meta-analyses and systematic reviews, and searched electronic bibliographic databases (from 2008 September 2012) including MEDLINE, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects, NHS Centre for Reviews and Dissemination, and Science Citation Index. We limited searches to publications since 2008, based on timing of the most recent comprehensive systematic reviews. Results: In total, 2572 potentially relevant papers were identified and 27 met the inclusion criteria. Benefits of aspirin ranged from 6% reduction in relative risk (RR) for all-cause mortality [RR 0.94, 95% confidence interval (CI) 0.88 to 1.00] and 10% reduction in major cardiovascular events (MCEs) (RR 0.90, 95% CI 0.85 to 0.96) to a reduction in total coronary heart disease (CHD) of 15% (RR 0.85, 95% CI 0.69 to 1.06). Reported pooled odds ratios (ORs) for total cancer mortality ranged between 0.76 (95% CI 0.66 to 0.88) and 0.93 (95% CI 0.84 to 1.03). Inclusion of the Women's Health Study changed the estimated OR to 0.82 (95% CI 0.69 to 0.97). Aspirin reduced reported colorectal cancer (CRC) incidence (OR 0.66, 95% CI 0.90 to 1.02). However, including studies in which aspirin was given every other day raised the OR to 0.91 (95% CI 0.74 to 1.11). Reported cancer benefits appeared approximately 5 years from start of treatment. Calculation of absolute effects per 100,000 patient-years of follow-up showed reductions ranging from 33 to 46 deaths (all-cause mortality), 60-84 MCEs and 47-64 incidents of CHD and a possible avoidance of 34 deaths from CRC. Reported increased RRs of adverse events from aspirin use were 37% for GI bleeding (RR 1.37, 95% CI 1.15 to 1.62), between 54% (RR 1.54, 95% CI 1.30 to 1.82) and 62% (RR 1.62, 95% CI 1.31 to 2.00) for major bleeds, and between 32% (RR 1.32, 95% CI 1.00 to 1.74) and 38% (RR 1.38, 95% CI 1.01 to 1.82) for haemorrhagic stroke. Pooled estimates of increased RR for bleeding remained stable across trials conducted over several decades. Estimates of absolute rates of harm from aspirin use, per 100,000 patient-years of follow-up, were 99-178 for non-trivial bleeds, 46-49 for major bleeds, 68-117 for GI bleeds and 8-10 for haemorrhagic stroke. Meta-analyses aimed at judging risk of bleed according to sex and in individuals with diabetes were insufficiently powered for firm conclusions to be drawn. Limitations: Searches were date limited to 2008 because of the intense interest that this subject has generated and the cataloguing of all primary research in so many previous systematic reviews. A further limitation was our potential over-reliance on study-level systematic reviews in which the person-years of follow-up were not accurately ascertainable. However, estimates of number of events averted or incurred through aspirin use calculated from data in study-level meta-analyses did not differ substantially from estimates based on individual patient data-level meta-analyses, for which person-years of follow-up were more accurate (although based on less-than-complete assemblies of currently available primary studies). Conclusions: We have found that there is a fine balance between benefits and risks from regular aspirin use in primary prevention of CVD. Effects on cancer prevention have a long lead time and are at present reliant on post hoc analyses. All absolute effects are relatively small compared with the burden of these diseases. Several potentially relevant ongoing trials will be completed between 2013 and 2019, which may clarify the extent of benefit of aspirin in reducing cancer incidence and mortality. Future research considerations include expanding the use of IPD meta-analysis of RCTs by pooling data from available studies and investigating the impact of different dose regimens on cardiovascular and cancer outcomes

RGB-D-based Action Recognition Datasets: A Survey

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against state-of-the-art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi-view datasets, and 7 multi-person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis-\'{a}-vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols